Lineage tracing for general data warehouse transformations
The VLDB Journal — The International Journal on Very Large Data Bases
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ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
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Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Access Methods for Markovian Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
ZStream: a cost-based query processor for adaptively detecting composite events
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Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PODS: a new model and processing algorithms for uncertain data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Lineage processing over correlated probabilistic databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Lahar: warehousing markovian streams
Lahar: warehousing markovian streams
Specification and verification of complex location events with panoramic
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
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Imprecise, sequential data, such as location sequences inferred from RFID/GPS, are often represented as Markovian (probabilistic, temporally-correlated) streams. Event queries, which detect instances of specific patterns in these streams, have become the standard tool for analysis of these streams; however, many data mining applications require richer information such as how a pattern is matched, how long the match is, or what stream elements matched specific pattern predicates. Such queries can dramatically increase the power of applications, but they cannot be answered by existing tools. In this paper, we present novel techniques for processing the above queries on Markovian streams. Central to our approach are algorithms for computing and manipulating the lineage of Markovian stream event queries. We provide formal definitions and linear-time algorithms for computing lineage, which may be exponentially-sized in the length of the input stream. We additionally demonstrate the importance of flexible lineage projections, and provide definitions of, and two efficient algorithms for, these projections. We evaluate all algorithms on two real-world data sets (location from RFID and words from spoken audio), and demonstrate that lineage can greatly increase the analytical power of applications while incurring small processing overhead.